数据挖掘 - scikit-learn 中的 cross_validate 会自动拟合和训练模型吗？ - 吾爱随笔录

# from the titanic dataset
X = df.drop(columns="survived")
y = df.survived
scoring = ['accuracy','precision','roc_auc','f1',]
from sklearn.model_selection import cross_validate
from sklearn.linear_model import (LogisticRegression)
def model_LR(): #logstic Regression
    index = ["kfold-1","kfold-2","kfold-3","kfold-4","kfold-5"]
    s = model_selection.cross_validate(LogisticRegression(), X, y, scoring = scoring, cv = 5 )
    s = pd.DataFrame(data = s, index = index)
    display (s)
    print ("The mean scores for the above:\n", s.mean())

model_LR()

# OUTPUT :
    fit_time    score_time  test_accuracy   test_precision  test_roc_auc    test_f1
kfold-1 0.003998    0.006969    0.774809    0.711340    0.823673    0.700508
kfold-2 0.003990    0.005005    0.820611    0.778947    0.856481    0.758974
kfold-3 0.003003    0.003989    0.774809    0.715789    0.796667    0.697436
kfold-4 0.002992    0.003992    0.767176    0.709677    0.841852    0.683938
kfold-5 0.001994    0.003989    0.819923    0.819277    0.877081    0.743169
The mean scores for the above:
 fit_time          0.003195
score_time        0.004789
test_accuracy     0.791466
test_precision    0.747006
test_roc_auc      0.839151
test_f1           0.716805
dtype: float64

我的问题：在上面的代码中，通过使用 LogisticsRegression 调用 cross_validate，我得到分数（例如 roc_auc），就好像模型已经过拟合和训练一样。
我没有使用任何适合或训练功能。这是否意味着 cross_validate 会自动执行此操作？谢谢。