如何在Python中使用StratifiedKFold在LogisticRegression中进行参数调优？

from sklearn.model_selection import StratifiedKFold, GridSearchCV
from sklearn.linear_model import LogisticRegression

param_grid = {'C': [0.1, 1, 10, 100], 'penalty': ['l1', 'l2']}

skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)
lr = LogisticRegression()

grid_search = GridSearchCV(estimator=lr, param_grid=param_grid, cv=skf, scoring='accuracy')

grid_search.fit(X, y)

best_params = grid_search.best_params_
best_model = grid_search.best_estimator_
print("Best parameters:", best_params)
print("Best model:", best_model)