GridSearchCV results are different to directly applied default model (SVM) I run a Support Vector Machines model on part of my train set with following result:

alg = sk.svm.SVC(probability=True, gamma='auto')
cv_results = model_selection.cross_validate(alg, X_pca, labels, cv =4)




but when I am trying to tune the parameters, with following method:

model=sk.svm.SVC()
params = {'C' : [0.01, 0.1, 1, 10],
      'gamma' : [0.1, 1, 'auto'],
      'probability' : [True] 
     }
clf =  GridSearchCV(model, params, cv=2, return_train_score=False).fit(X_pca, labels)
pd.DataFrame(clf.cv_results_).loc[:, ['mean_test_score', 'rank_test_score', 'params']].sort_values(by='rank_test_score')




So not only all results looks scetchy because they are the same. but also in one of the rows I have C:1, gamma:auto and probability: True which is the same parameters as in first table.

I want to also say, that the same logic I am using for the rest of my 15 ML algorithms and only SVM showed this kind of weird behavior. Wondering that maybe I have some stupid mistake in how I create X_pca and labels data table, I copied code from other algorithm and just replaced second code but it gave the same results. 

Can you spot something wrong?
I think the problem might be due to the data, as this code:

from sklearn import svm
from sklearn.model_selection import GridSearchCV
import pandas as pd
import numpy as np

X_pca = np.random.rand(100, 2)
labels = X_pca[:, 0] + X_pca[:, 1] &gt; 0.5

model = svm.SVC()
params = {'C' : [0.01, 0.1, 1, 10],
      'gamma' : [0., 0.1, 1, 'auto'],
      'probability' : [True] 
     }
clf =  GridSearchCV(model, params, cv=2, return_train_score=False)
clf.fit(X_pca, labels)

print(pd.DataFrame(clf.cv_results_).loc[:, ['mean_test_score', 'rank_test_score']] \
        .sort_values(by='rank_test_score'))


gives better output:

    mean_test_score  rank_test_score
10             1.00                1
13             1.00                1
14             1.00                1
15             1.00                1
11             0.95                5
0              0.83                6
1              0.83                6
2              0.83                6
3              0.83                6
4              0.83                6
5              0.83                6
6              0.83                6
7              0.83                6
8              0.83                6
9              0.83                6
12             0.83                6


(consider the fact that the results will depend on the seed of the rand function, but with other seeds they are similar and the mean test score changes using different values of the parameters.