Well, grid search involves finding best hyperparameters. Best according to what data set? a held out validation set. If that's what you mean by cross validation, then they necessarily happen simultaneously.
It doesn't really make sense to do something called cross validation before testing hyperparams - indeed, what would you be evaluating?
CV as in k-fold cross validation can also happen within each model fitting process in the search, to produce a better estimate of the loss (and its variance, which is useful in more sophisticated tuning procedures). I think this is less usual but valid.
It's possible to use CV when fitting the final model after hyperparameter search. It might give you a better estimate of the loss, or confusion matrix, as you compute many of them. But each model you fit isn't using all available data. I think it's probably more conventional to take the best model's parameters and loss / confusion matrix, from the fitting process, as an estimate of generalization, and then refit the final model on all data. This means no CV at that stage.