If you cannot acquire a workable test data-set to test a model, you can use cross validation as an alternative to validate the model.
However, I'm unsure of the true end purpose of cross validation...
Is CV useful for simply generating a performance metric by providing a way to test your model? Or is it a means to creating a final model?
Put differently: do I use my "best" model from whichever given fold of the CV process (i.e., the "best" model using some partition of the data) as my final model? Or do I perform CV, report my performance metric, and then re-create the model using ALL of my available data to create a final model?