diff --git a/sklearn/covariance/_shrunk_covariance.py b/sklearn/covariance/_shrunk_covariance.py
index 72f5101f4d753..bba1a6216bb56 100644
--- a/sklearn/covariance/_shrunk_covariance.py
+++ b/sklearn/covariance/_shrunk_covariance.py
@@ -18,11 +18,33 @@
 import numpy as np
 
 from . import empirical_covariance, EmpiricalCovariance
-from .._config import config_context
 from ..utils import check_array
-from ..utils._param_validation import Interval
+from ..utils._param_validation import Interval, validate_params
 
 
+def _ledoit_wolf(X, *, assume_centered, block_size):
+    """Estimate the shrunk Ledoit-Wolf covariance matrix."""
+    # for only one feature, the result is the same whatever the shrinkage
+    if len(X.shape) == 2 and X.shape[1] == 1:
+        if not assume_centered:
+            X = X - X.mean()
+        return np.atleast_2d((X**2).mean()), 0.0
+    n_features = X.shape[1]
+
+    # get Ledoit-Wolf shrinkage
+    shrinkage = ledoit_wolf_shrinkage(
+        X, assume_centered=assume_centered, block_size=block_size
+    )
+    emp_cov = empirical_covariance(X, assume_centered=assume_centered)
+    mu = np.sum(np.trace(emp_cov)) / n_features
+    shrunk_cov = (1.0 - shrinkage) * emp_cov
+    shrunk_cov.flat[:: n_features + 1] += shrinkage * mu
+
+    return shrunk_cov, shrinkage
+
+
+###############################################################################
+# Public API
 # ShrunkCovariance estimator
 
 
@@ -288,6 +310,7 @@ def ledoit_wolf_shrinkage(X, assume_centered=False, block_size=1000):
     return shrinkage
 
 
+@validate_params({"X": ["array-like"]})
 def ledoit_wolf(X, *, assume_centered=False, block_size=1000):
     """Estimate the shrunk Ledoit-Wolf covariance matrix.
 
@@ -325,31 +348,13 @@ def ledoit_wolf(X, *, assume_centered=False, block_size=1000):
 
     where mu = trace(cov) / n_features
     """
-    X = check_array(X)
-    # for only one feature, the result is the same whatever the shrinkage
-    if len(X.shape) == 2 and X.shape[1] == 1:
-        if not assume_centered:
-            X = X - X.mean()
-        return np.atleast_2d((X**2).mean()), 0.0
-    if X.ndim == 1:
-        X = np.reshape(X, (1, -1))
-        warnings.warn(
-            "Only one sample available. You may want to reshape your data array"
-        )
-        n_features = X.size
-    else:
-        _, n_features = X.shape
+    estimator = LedoitWolf(
+        assume_centered=assume_centered,
+        block_size=block_size,
+        store_precision=False,
+    ).fit(X)
 
-    # get Ledoit-Wolf shrinkage
-    shrinkage = ledoit_wolf_shrinkage(
-        X, assume_centered=assume_centered, block_size=block_size
-    )
-    emp_cov = empirical_covariance(X, assume_centered=assume_centered)
-    mu = np.sum(np.trace(emp_cov)) / n_features
-    shrunk_cov = (1.0 - shrinkage) * emp_cov
-    shrunk_cov.flat[:: n_features + 1] += shrinkage * mu
-
-    return shrunk_cov, shrinkage
+    return estimator.covariance_, estimator.shrinkage_
 
 
 class LedoitWolf(EmpiricalCovariance):
@@ -488,10 +493,9 @@ def fit(self, X, y=None):
             self.location_ = np.zeros(X.shape[1])
         else:
             self.location_ = X.mean(0)
-        with config_context(assume_finite=True):
-            covariance, shrinkage = ledoit_wolf(
-                X - self.location_, assume_centered=True, block_size=self.block_size
-            )
+        covariance, shrinkage = _ledoit_wolf(
+            X - self.location_, assume_centered=True, block_size=self.block_size
+        )
         self.shrinkage_ = shrinkage
         self._set_covariance(covariance)
 
diff --git a/sklearn/covariance/tests/test_covariance.py b/sklearn/covariance/tests/test_covariance.py
index 6a9031d0fcb36..fb4eeb26138df 100644
--- a/sklearn/covariance/tests/test_covariance.py
+++ b/sklearn/covariance/tests/test_covariance.py
@@ -7,6 +7,7 @@
 import numpy as np
 import pytest
 
+from sklearn.utils._testing import assert_allclose
 from sklearn.utils._testing import assert_almost_equal
 from sklearn.utils._testing import assert_array_almost_equal
 from sklearn.utils._testing import assert_array_equal
@@ -23,6 +24,7 @@
     OAS,
     oas,
 )
+from sklearn.covariance._shrunk_covariance import _ledoit_wolf
 
 X, _ = datasets.load_diabetes(return_X_y=True)
 X_1d = X[:, 0]
@@ -158,6 +160,9 @@ def test_ledoit_wolf():
     assert_almost_equal(lw.shrinkage_, shrinkage_, 4)
     assert_almost_equal(lw.shrinkage_, ledoit_wolf_shrinkage(X))
     assert_almost_equal(lw.shrinkage_, ledoit_wolf(X)[1])
+    assert_almost_equal(
+        lw.shrinkage_, _ledoit_wolf(X=X, assume_centered=False, block_size=10000)[1]
+    )
     assert_almost_equal(lw.score(X), score_, 4)
     # compare shrunk covariance obtained from data and from MLE estimate
     lw_cov_from_mle, lw_shrinkage_from_mle = ledoit_wolf(X)
@@ -172,6 +177,10 @@ def test_ledoit_wolf():
     X_1d = X[:, 0].reshape((-1, 1))
     lw = LedoitWolf()
     lw.fit(X_1d)
+    assert_allclose(
+        X_1d.var(ddof=0),
+        _ledoit_wolf(X=X_1d, assume_centered=False, block_size=10000)[0],
+    )
     lw_cov_from_mle, lw_shrinkage_from_mle = ledoit_wolf(X_1d)
     assert_array_almost_equal(lw_cov_from_mle, lw.covariance_, 4)
     assert_almost_equal(lw_shrinkage_from_mle, lw.shrinkage_)
diff --git a/sklearn/tests/test_public_functions.py b/sklearn/tests/test_public_functions.py
index b42780077f1ce..3314cb372627d 100644
--- a/sklearn/tests/test_public_functions.py
+++ b/sklearn/tests/test_public_functions.py
@@ -130,6 +130,7 @@ def test_function_param_validation(func_module):
 
 PARAM_VALIDATION_CLASS_WRAPPER_LIST = [
     ("sklearn.decomposition.non_negative_factorization", "sklearn.decomposition.NMF"),
+    ("sklearn.covariance.ledoit_wolf", "sklearn.covariance.LedoitWolf"),
 ]