how to prepare data for cross validation in mnist dataset? How to use k-fold cross validation for MNIST dataset?
I read article documentation on sci-kit learn ,in that example they used the whole iris dataset for cross validation.

from sklearn.model_selection import cross_val_score
clf = svm.SVC(kernel='linear', C=1)
scores = cross_val_score(clf, iris.data, iris.target, cv=5)
scores                                              


for example while importing mnist dataset in keras 

from keras.datasets import mnist
(Xtrain,Ytrain),(Xtest,Ytest)=mnist_load()


in this dataset is already divided in test and train , so to apply cross validation on the entire dataset do we need to make Xtrain and Xtest as one entity to exploit the whole data.
from sklearn.model_selection import cross_val_score
clf = svm.SVC(kernel='linear', C=1)
scores = cross_val_score(clf, iris.data, iris.target, cv=5)
scores


They are not using the whole data for cross validation as such ( it's just an illusion)

When the cv argument is an integer, cross_val_score uses the KFold or StratifiedKFold strategies by default, the latter being used if the estimator derives from ClassifierMixin..

So it's kind of automated inside the call..

Check this kaggle kernel link