cross validation issues
Cross validation is basically for hyper-parameter tuning. 

You train a set of model hyper-parameter setting on kfold cross validation and take the average score from kfold cross validation as the approximate performance of each model hyper-parameter setting. Then, the model hyper-parameter setting which has the highest score will be the choice of your model setting. This hyper-parameter setting can be treat as the best you can get from this kind of model. 

Later, you can use this model setting to test the general performance on your test dataset.