crossvalidation purpose is to run the models with same set of parameters several times to get the average performance. each fold starts fresh with a new copy of the model using the same set of parameters. if for example among the models, SVC is the best, then you can use the fit! to train the SVC model for a particular training data, and use transform! to use the said model for prediction feeding it a new testing data. there is no point in crossvalidation to save the model because what it does is to estimate the average performance of a model given different splits of the data. crossvalidation basically use fit! and transform! for each training/testing fold.