@Bertffm We can use the process of cross validation (CV) to obtain a relatively stable performance estimate for a model instead of improving it.

Think of trying out different model types and parametrizations which are differently well suited for your problem. Using CV you obtain many different estimates on how each model type and parametrization would perform on unseen data. From those results you usually choose one well suited model type + parametrization which you will use, then train it again on all (training) data. The aim of CV is to find best fit parameters giving better results. So averaging seems not a solution here.

You mentioned taking the average of models. Its one way to use an ensemble of models instead of a single one. But also for those you want to use CV in the end for choosing reasonable model.

Hope this helps.