在GridSearchCV中如何/在何处使用StandardScaler

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import GridSearchCV
from sklearn.svm import SVC

# 创建一个Pipeline对象，将StandardScaler作为一个步骤添加到Pipeline中
pipeline = Pipeline([
    ('scaler', StandardScaler()),
    ('clf', SVC())
])

# 定义参数网格
param_grid = {
    'clf__C': [0.1, 1, 10],
    'clf__kernel': ['linear', 'rbf']
}

# 创建GridSearchCV对象，指定Pipeline和参数网格
grid_search = GridSearchCV(pipeline, param_grid=param_grid)

# 使用GridSearchCV进行模型训练和参数调优
grid_search.fit(X_train, y_train)

# 输出最佳模型的参数和评分
print("Best parameters found: ", grid_search.best_params_)
print("Best score found: ", grid_search.best_score_)