[Python] StratifiedKFold vs KFold in scikit-learn

I use this code to test KFold and StratifiedKFold.

import numpy as np
from sklearn.model_selection import KFold,StratifiedKFold

X = np.array([
[1,2,3,4],
[11,12,13,14],
[21,22,23,24],
[31,32,33,34],
[41,42,43,44],
[51,52,53,54],
[61,62,63,64],
[71,72,73,74]
])

y = np.array([0,0,0,0,1,1,1,1])

sfolder = StratifiedKFold(n_splits=4,random_state=0,shuffle=False)
floder = KFold(n_splits=4,random_state=0,shuffle=False)

for train, test in sfolder.split(X,y):
print('Train: %s | test: %s' % (train, test))
print("StratifiedKFold done")

for train, test in floder.split(X,y):
print('Train: %s | test: %s' % (train, test))
print("KFold done")


I found that StratifiedKFold can keep the proportion of labels, but KFold can't.

Train: [1 2 3 5 6 7] | test: [0 4]
Train: [0 2 3 4 6 7] | test: [1 5]
Train: [0 1 3 4 5 7] | test: [2 6]
Train: [0 1 2 4 5 6] | test: [3 7]
StratifiedKFold done
Train: [2 3 4 5 6 7] | test: [0 1]
Train: [0 1 4 5 6 7] | test: [2 3]
Train: [0 1 2 3 6 7] | test: [4 5]
Train: [0 1 2 3 4 5] | test: [6 7]
KFold done


It seems that StratifiedKFold is better, so should KFold not be used?

When to use KFold instead of StratifiedKFold?

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