At what stage are ROC curves used when building machine learning model?
The ROC-AUC curves are used to find the best threshold that optimizes True Positive Rate vs False Positive Rate. Using it in a K-Fold cross-validation is a good practice to determine the best threshold to use.
Then, your final test is here to validate that you did not overfit on some hyperparameters, including this threshold. So ROC-AUC must not be used again in final test. You should compare the results of your final test with the same threshold used in your cross-validation.
Hope it helps.
Note on threshold (EDIT):
The threshold to optimize could be the threshold to use in a binary classification problem that outputs probabilities (for instance, output of a sigmoid or a logistic regression). In that case, various threshold settings gives various the model's predictions (FPR, TPR), and so is built the ROC curve.
You could read further on sklearn guide page.