GridSearchCV results are different to directly applied default model (SVM)
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.