diff --git a/sklearn/linear_model/_ridge.py b/sklearn/linear_model/_ridge.py
index 827366fab2a25..fda2e778b17c4 100644
--- a/sklearn/linear_model/_ridge.py
+++ b/sklearn/linear_model/_ridge.py
@@ -2248,12 +2248,7 @@ def _score(self, *, predictions, y, n_y, scorer, score_params):
                     ]
                 )
             else:
-                _score = scorer(
-                    identity_estimator,
-                    predictions.ravel(),
-                    y.ravel(),
-                    **score_params,
-                )
+                _score = scorer(identity_estimator, predictions, y, **score_params)
 
         return _score
 
diff --git a/sklearn/linear_model/tests/test_ridge.py b/sklearn/linear_model/tests/test_ridge.py
index 008ccf11d6ac3..cb42e1a473647 100644
--- a/sklearn/linear_model/tests/test_ridge.py
+++ b/sklearn/linear_model/tests/test_ridge.py
@@ -2252,6 +2252,32 @@ def test_ridge_cv_values_deprecated():
         ridge.cv_values_
 
 
+def test_ridge_cv_multioutput_sample_weight(global_random_seed):
+    """Check that `RidgeCV` works properly with multioutput and sample_weight
+    when `scoring != None`.
+
+    We check the error reported by the RidgeCV is close to a naive LOO-CV using a
+    Ridge estimator.
+    """
+    X, y = make_regression(n_targets=2, random_state=global_random_seed)
+    sample_weight = np.ones(shape=(X.shape[0],))
+
+    ridge_cv = RidgeCV(scoring="neg_mean_squared_error", store_cv_results=True)
+    ridge_cv.fit(X, y, sample_weight=sample_weight)
+
+    cv = LeaveOneOut()
+    ridge = Ridge(alpha=ridge_cv.alpha_)
+    y_pred_loo = np.squeeze(
+        [
+            ridge.fit(X[train], y[train], sample_weight=sample_weight[train]).predict(
+                X[test]
+            )
+            for train, test in cv.split(X)
+        ]
+    )
+    assert_allclose(ridge_cv.best_score_, -mean_squared_error(y, y_pred_loo))
+
+
 # Metadata Routing Tests
 # ======================