bobo老师，你好
我试图用线性回归模型对波士顿房价数据进行预测，我试了两种方法建立模型，
第一种：

pip_clf = Pipeline([
    ("scaler", StandardScaler()),
    ("lin_reg", LinearRegression())
])

#精度为：0.8111288663608667
第二种：

gridsearch = GridSearchCV(pip_clf, param_grid={},cv=5)

#精度为：0.3532759243958822

为什么网格搜索的结果要差这么多呢？网格搜索准确度不是应该高一些吗
完整代码如下：

在这里输入代码
import numpy as np
from sklearn import datasets
from sklearn.linear_model import Ridge
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.model_selection import train_test_split

boston = datasets.load_boston()

x = boston.data
y = boston.target

X_train, X_test, y_train, y_test = train_test_split(x, y, random_state=66, test_size = 0.2)

pip_clf = Pipeline([
    ("scaler", StandardScaler()),
    ("lin_reg", LinearRegression())
])

#方法一
pip_clf.fit(X_train, y_train)
pip_clf.score(X_test, y_test)
#0.8111288663608667
#方法一

gridsearch = GridSearchCV(pip_clf, param_grid={},cv=5)
gridsearch.fit(x, y)
gridsearch.best_score_
#0.3532759243958822