Early stopping with Keras and sklearn GridSearchCV cross-validation
[Old answer, before the question was edited & clarified - see updated & accepted answer above]
I am not sure I have understood your exact issue (your question is quite unclear, and you include many unrelated details, which is never good when asking a SO question - see here).
You don't have to (and actually should not) include any arguments about validation data in your model = KerasClassifier() function call (it is interesting why you don't feel the same need for training data here, too). Your grid.fit() will take care of both the training and validation folds. So provided that you want to keep the hyperparameter values as included in your example, this function call should be simply
model = KerasClassifier(build_fn=create_model,
epochs=100, batch_size=32,
shuffle=True,
verbose=1)
You can see some clear and well-explained examples regarding the use of GridSearchCV with Keras here.
[Answer after the question was edited & clarified:]
Before rushing into implementation issues, it is always a good practice to take some time to think about the methodology and the task itself; arguably, intermingling early stopping with the cross validation procedure is not a good idea.
Let's make up an example to highlight the argument.
Suppose that you indeed use early stopping with 100 epochs, and 5-fold cross validation (CV) for hyperparameter selection. Suppose also that you end up with a hyperparameter set X giving best performance, say 89.3% binary classification accuracy.
Now suppose that your second-best hyperparameter set, Y, gives 89.2% accuracy. Examining closely the individual CV folds, you see that, for your best case X, 3 out of the 5 CV folds exhausted the max 100 epochs, while in the other 2 early stopping kicked in, say in 95 and 93 epochs respectively.
Now imagine that, examining your second-best set Y, you see that again 3 out of the 5 CV folds exhausted the 100 epochs, while the other 2 both stopped early enough at ~ 80 epochs.
What would be your conclusion from such an experiment?
Arguably, you would have found yourself in an inconclusive situation; further experiments might reveal which is actually the best hyperparameter set, provided of course that you would have thought to look into these details of the results in the first place. And needless to say, if all this was automated through a callback, you might have missed your best model despite the fact that you would have actually tried it.
The whole CV idea is implicitly based on the "all other being equal" argument (which of course is never true in practice, only approximated in the best possible way). If you feel that the number of epochs should be a hyperparameter, just include it explicitly in your CV as such, rather than inserting it through the back door of early stopping, thus possibly compromising the whole process (not to mention that early stopping has itself a hyperparameter, patience).
Not intermingling these two techniques doesn't mean of course that you cannot use them sequentially: once you have obtained your best hyperparameters through CV, you can always employ early stopping when fitting the model in your whole training set (provided of course that you do have a separate validation set).
The field of deep neural nets is still (very) young, and it is true that it has yet to establish its "best practice" guidelines; add the fact that, thanks to an amazing community, there are all sort of tools available in open source implementations, and you can easily find yourself into the (admittedly tempting) position of mixing things up just because they happen to be available. I am not necessarily saying that this is what you are attempting to do here - I am just urging for more caution when combining ideas that may have not been designed to work along together...