diff --git a/ax/analysis/plotly/cross_validation.py b/ax/analysis/plotly/cross_validation.py
index 25fd5483455..c93445d3f48 100644
--- a/ax/analysis/plotly/cross_validation.py
+++ b/ax/analysis/plotly/cross_validation.py
@@ -14,6 +14,7 @@
 from ax.core.experiment import Experiment
 from ax.exceptions.core import UserInputError
 from ax.generation_strategy.generation_strategy import GenerationStrategy
+from ax.modelbridge.base import Adapter
 from ax.modelbridge.cross_validation import cross_validate
 from plotly import express as px, graph_objects as go
 from pyre_extensions import none_throws
@@ -89,18 +90,108 @@ def compute(
         metric_name = self.metric_name or select_metric(
             experiment=generation_strategy.experiment
         )
+        # If model is not fit already, fit it
+        if generation_strategy.model is None:
+            generation_strategy._fit_current_model(None)
 
-        df = _prepare_data(
-            generation_strategy=generation_strategy,
+        return self._construct_plot(
+            adapter=none_throws(generation_strategy.model),
             metric_name=metric_name,
             folds=self.folds,
             untransform=self.untransform,
             trial_index=self.trial_index,
+            experiment=experiment,
         )
-        fig = _prepare_plot(df=df)
 
-        k_folds_substring = f"{self.folds}-fold" if self.folds > 0 else "leave-one-out"
+    def _compute_adhoc(
+        self,
+        adapter: Adapter,
+        metric_name: str,
+        experiment: Experiment | None = None,
+        folds: int = -1,
+        untransform: bool = True,
+    ) -> PlotlyAnalysisCard:
+        """
+        Helper method to expose adhoc cross validation plotting. This overrides the
+        default assumption that the adapter from the generation strategy should be
+        used. Only for advanced users in a notebook setting.
+
+        Args:
+            adapter: The adapter that will be assessed during cross validation.
+            metric_name: The name of the metric to plot. Must be provided for adhoc
+                plotting.
+            experiment: Experiment associated with this analysis. Used to determine
+                the priority of the analysis based on the metric importance in the
+                optimization config.
+            folds: Number of subsamples to partition observations into. Use -1 for
+                leave-one-out cross validation.
+            untransform: Whether to untransform the model predictions before cross
+                validating. Generators are trained on transformed data, and candidate
+                generation is performed in the transformed space. Computing the model
+                quality metric based on the cross-validation results in the
+                untransformed space may not be representative of the model that
+                is actually used for candidate generation in case of non-invertible
+                transforms, e.g., Winsorize or LogY. While the model in the
+                transformed space may not be representative of the original data in
+                regions where outliers have been removed, we have found it to better
+                reflect the how good the model used for candidate generation actually
+                is.
+        """
+        return self._construct_plot(
+            adapter=adapter,
+            metric_name=metric_name,
+            folds=folds,
+            untransform=untransform,
+            # trial_index argument is used with generation strategy since this is an
+            # adhoc plot call, this will be None.
+            trial_index=None,
+            experiment=experiment,
+        )
 
+    def _construct_plot(
+        self,
+        adapter: Adapter,
+        metric_name: str,
+        folds: int,
+        untransform: bool,
+        trial_index: int | None,
+        experiment: Experiment | None = None,
+    ) -> PlotlyAnalysisCard:
+        """
+        Args:
+            adapter: The adapter that will be assessed during cross validation.
+            metric_name: The name of the metric to plot.
+            folds: Number of subsamples to partition observations into. Use -1 for
+                leave-one-out cross validation.
+            untransform: Whether to untransform the model predictions before cross
+                validating. Generators are trained on transformed data, and candidate
+                generation is performed in the transformed space. Computing the model
+                quality metric based on the cross-validation results in the
+                untransformed space may not be representative of the model that
+                is actually used for candidate generation in case of non-invertible
+                transforms, e.g., Winsorize or LogY. While the model in the
+                transformed space may not be representative of the original data in
+                regions where outliers have been removed, we have found it to better
+                reflect the how good the model used for candidate generation actually
+                is.
+            trial_index: Optional trial index that the model from generation_strategy
+                was used to generate.  We should therefore only have observations from
+                trials prior to this trial index in our plot.  If this is not True, we
+                should error out.
+            experiment: Optional Experiment associated with this analysis. Used to set
+                the priority of the analysis based on the metric importance in the
+                optimization config.
+        """
+        df = _prepare_data(
+            adapter=adapter,
+            metric_name=metric_name,
+            folds=folds,
+            untransform=untransform,
+            trial_index=trial_index,
+        )
+
+        fig = _prepare_plot(df=df)
+        k_folds_substring = f"{folds}-fold" if folds > 0 else "leave-one-out"
         # Nudge the priority if the metric is important to the experiment
         if (
             experiment is not None
@@ -129,18 +220,14 @@ def compute(
 
 
 def _prepare_data(
-    generation_strategy: GenerationStrategy,
+    adapter: Adapter,
     metric_name: str,
     folds: int,
     untransform: bool,
     trial_index: int | None,
 ) -> pd.DataFrame:
-    # If model is not fit already, fit it
-    if generation_strategy.model is None:
-        generation_strategy._fit_current_model(None)
-
     cv_results = cross_validate(
-        model=none_throws(generation_strategy.model),
+        model=adapter,
         folds=folds,
         untransform=untransform,
     )