You should provide more details about your study. Since you are talking about a final model, I assume you are running a step-wise regression. In such a regression, the final model is the model with the best indicators of model fit, determined blindly by the computer algorithm. So it consists of one dependent variable and a number of selected independent variables that are in the highest association with that dependent variable.

Since you are talking about training and test sets, I assume you want to fit a model and then test its prediction merit. In this case, the model is created based on the training set (and not the test set). What is the model here? A dependent and a number of independent variables, plus their beta values and standard errors. Now you will need to apply this model to the test set and see how effectively can it predict the dependent variable, based on the values assigned to the independent variables.

Cross validation is a method to determine the predictive value of the model, without any test sets. Therefore, you can use cross validation to optimize the model further (over the training set), before applying it to the test set.

Again, you should provide more details regarding your study, variables, etc.