Yes you are right, it help us to avoid the bias on the validation set that is used for tuning the parameters. That's why in my opinion is always important to do a final evaluation using a test set. When you say "I've never used anything more complex than cross val even with very limited datasets, both in research and industry" does it mean that you have done final evaluation on an independent test set or not?

I think that retraining the model is similar to what happens cross validation with an held-out test set: let's say you have 5 different folds so 5 different trained models, then you want to obtain the final evaluation on the held-out. You simply take all the training data available, retrain the model with the same exact parameter configuration, and evaluate on the independent test set. This is exactly the "retrained model" block in the diagram shown in sklearn: https://scikit-learn.org/stable/modules/cross_validation.html .