Python and GridSearchCV how to eliminate input contains NaN error when using cross validation and decision tree classifier?
You should impute missing values, try with:

from sklearn.impute import SimpleImputer
imp = SimpleImputer(missing_values=np.nan, strategy='mean')
imp.fit(x_train)
x_train = imp.transform(x_train)
x_test = imp.transform(x_test)


Notice that I am fiting just in the train data, so you are not leaking information to the test.