How to choose a predictive model after k-fold cross-validation?

I am wondering how to choose a predictive model after doing K-fold cross-validation. This may be awkwardly phrased, so let me explain in more detail: whenever I run K-fold cross-validation, I use K subsets of the training data, and end up with K different models. I would like to know how to pick one of the K models, so that I can present it to someone and say "this is the best model that we can produce." Is it OK to pick any one of the K models? Or is there some kind of best practice that is involved, such as picking the model that achieves the median test error?