How to select the learned model using $k$-fold cross validation?
$k$-fold is just performed to obtain a measure of your accuracy, as using the training accuracy is generally a too optimistic measure of the accuracy. If you want to deploy a final model, what is recommended is to train a last model with all the data. In fact, when you compare two models, $f$ and $g$, what you do is obtain two error rates $r_f$ and $r_g$ by cross-validation, and you keep the model with the lowest error rate. After that, if the model with the lowest error rate is $f$, you retrain $f$ with all your data.

To sum up, $k$-fold cross-validation is a method to give measures of the performance of the model, if you want to obtain the best model just train it with all your data.