diff --git a/sklearn/model_selection/_validation.py b/sklearn/model_selection/_validation.py
index 6bb4e2270f359..60276db8ca361 100644
--- a/sklearn/model_selection/_validation.py
+++ b/sklearn/model_selection/_validation.py
@@ -1818,6 +1818,23 @@ def _incremental_fit_estimator(
     return np.array(ret).T
 
 
+@validate_params(
+    {
+        "estimator": [HasMethods(["fit"])],
+        "X": ["array-like", "sparse matrix"],
+        "y": ["array-like", None],
+        "param_name": [str],
+        "param_range": ["array-like"],
+        "groups": ["array-like", None],
+        "cv": ["cv_object"],
+        "scoring": [StrOptions(set(get_scorer_names())), callable, None],
+        "n_jobs": [Integral, None],
+        "pre_dispatch": [Integral, str],
+        "verbose": ["verbose"],
+        "error_score": [StrOptions({"raise"}), Real],
+        "fit_params": [dict, None],
+    }
+)
 def validation_curve(
     estimator,
     X,
@@ -1847,10 +1864,12 @@ def validation_curve(
 
     Parameters
     ----------
-    estimator : object type that implements the "fit" and "predict" methods
-        An object of that type which is cloned for each validation.
+    estimator : object type that implements the "fit" method
+        An object of that type which is cloned for each validation. It must
+        also implement "predict" unless `scoring` is a callable that doesn't
+        rely on "predict" to compute a score.
 
-    X : array-like of shape (n_samples, n_features)
+    X : {array-like, sparse matrix} of shape (n_samples, n_features)
         Training vector, where `n_samples` is the number of samples and
         `n_features` is the number of features.
 
diff --git a/sklearn/model_selection/tests/test_validation.py b/sklearn/model_selection/tests/test_validation.py
index 2c275af617e40..08721921d872a 100644
--- a/sklearn/model_selection/tests/test_validation.py
+++ b/sklearn/model_selection/tests/test_validation.py
@@ -2118,17 +2118,6 @@ def test_fit_and_score_failing():
     with pytest.raises(ValueError, match=error_message):
         learning_curve(failing_clf, X, y, cv=3, error_score="unvalid-string")
 
-    with pytest.raises(ValueError, match=error_message):
-        validation_curve(
-            failing_clf,
-            X,
-            y,
-            param_name="parameter",
-            param_range=[FailingClassifier.FAILING_PARAMETER],
-            cv=3,
-            error_score="unvalid-string",
-        )
-
     assert failing_clf.score() == 0.0  # FailingClassifier coverage
 
 
diff --git a/sklearn/tests/test_public_functions.py b/sklearn/tests/test_public_functions.py
index 461b9364ca006..205a8efa9c9d8 100644
--- a/sklearn/tests/test_public_functions.py
+++ b/sklearn/tests/test_public_functions.py
@@ -258,6 +258,7 @@ def _check_function_param_validation(
     "sklearn.model_selection.cross_validate",
     "sklearn.model_selection.permutation_test_score",
     "sklearn.model_selection.train_test_split",
+    "sklearn.model_selection.validation_curve",
     "sklearn.neighbors.sort_graph_by_row_values",
     "sklearn.preprocessing.add_dummy_feature",
     "sklearn.preprocessing.binarize",