Machine Learning algorithms and Cross Validation, the best practice
Cross-validation (CV) is a method meant essentially to accurately evaluate a model using some training data. As a consequence CV doesn't have to be used when training the final model, usually one simply uses the whole training data for that. Note that using one of the CV models would have two disadvantages: there's no reason to select one or the other (the performance on one split is not relevant), and it doesn't exploit the whole training set.
I also notice that you didn't mention any test set evaluation in your steps. A common mistake is to select a model based on parameter tuning (step 3) using CV, and just assume that the CV performance is reliable. It is not because the fact that a choice was made (whether about parameters, models, features...) means that this step is still part of the training process, therefore there needs to be evaluation on a different test set afterwards. In other words: the fact that some parameters perform better than others could be due to chance, so it's important to evaluate the selected model (and only this model) on a fresh test set.