diff --git a/sklearn/datasets/_california_housing.py b/sklearn/datasets/_california_housing.py
index f3f7d0e57c502..98e3bfcb37a98 100644
--- a/sklearn/datasets/_california_housing.py
+++ b/sklearn/datasets/_california_housing.py
@@ -37,6 +37,7 @@
 from ._base import RemoteFileMetadata
 from ._base import load_descr
 from ..utils import Bunch
+from ..utils._param_validation import validate_params
 
 
 # The original data can be found at:
@@ -50,6 +51,14 @@
 logger = logging.getLogger(__name__)
 
 
+@validate_params(
+    {
+        "data_home": [str, None],
+        "download_if_missing": ["boolean"],
+        "return_X_y": ["boolean"],
+        "as_frame": ["boolean"],
+    }
+)
 def fetch_california_housing(
     *, data_home=None, download_if_missing=True, return_X_y=False, as_frame=False
 ):
diff --git a/sklearn/metrics/_ranking.py b/sklearn/metrics/_ranking.py
index ebe477a7e2de7..4ca4aa15257c5 100644
--- a/sklearn/metrics/_ranking.py
+++ b/sklearn/metrics/_ranking.py
@@ -34,7 +34,7 @@
 from ..utils.multiclass import type_of_target
 from ..utils.extmath import stable_cumsum
 from ..utils.sparsefuncs import count_nonzero
-from ..utils._param_validation import validate_params
+from ..utils._param_validation import validate_params, StrOptions
 from ..exceptions import UndefinedMetricWarning
 from ..preprocessing import label_binarize
 from ..utils._encode import _encode, _unique
@@ -112,6 +112,15 @@ def auc(x, y):
     return area
 
 
+@validate_params(
+    {
+        "y_true": ["array-like"],
+        "y_score": ["array-like"],
+        "average": [StrOptions({"micro", "samples", "weighted", "macro"}), None],
+        "pos_label": [Real, str, "boolean"],
+        "sample_weight": ["array-like", None],
+    }
+)
 def average_precision_score(
     y_true, y_score, *, average="macro", pos_label=1, sample_weight=None
 ):
@@ -137,10 +146,10 @@ def average_precision_score(
 
     Parameters
     ----------
-    y_true : ndarray of shape (n_samples,) or (n_samples, n_classes)
+    y_true : array-like of shape (n_samples,) or (n_samples, n_classes)
         True binary labels or binary label indicators.
 
-    y_score : ndarray of shape (n_samples,) or (n_samples, n_classes)
+    y_score : array-like of shape (n_samples,) or (n_samples, n_classes)
         Target scores, can either be probability estimates of the positive
         class, confidence values, or non-thresholded measure of decisions
         (as returned by :term:`decision_function` on some classifiers).
diff --git a/sklearn/tests/test_public_functions.py b/sklearn/tests/test_public_functions.py
index f2c19a53731d9..3f07a47fd4d1e 100644
--- a/sklearn/tests/test_public_functions.py
+++ b/sklearn/tests/test_public_functions.py
@@ -100,6 +100,7 @@ def _check_function_param_validation(
     "sklearn.cluster.kmeans_plusplus",
     "sklearn.covariance.empirical_covariance",
     "sklearn.covariance.shrunk_covariance",
+    "sklearn.datasets.fetch_california_housing",
     "sklearn.datasets.make_sparse_coded_signal",
     "sklearn.decomposition.sparse_encode",
     "sklearn.feature_extraction.grid_to_graph",
@@ -107,6 +108,7 @@ def _check_function_param_validation(
     "sklearn.feature_extraction.image.extract_patches_2d",
     "sklearn.metrics.accuracy_score",
     "sklearn.metrics.auc",
+    "sklearn.metrics.average_precision_score",
     "sklearn.metrics.cohen_kappa_score",
     "sklearn.metrics.confusion_matrix",
     "sklearn.metrics.det_curve",