With a high sample:predictor (n:p) ratio, as opposed to nested CV, why not just go with the split sample approach in which CV is done on training data (e.g., 80%) for model selection and estimation of the generalization error is done on the held out test data (e.g., 20%). The optimization bias at this point should be eliminated since model selection is separated from performance evaluation, so what is the benefit?
The benefit is that you get a better estimate of the model performance as
- it is based on absolutely more tested cases (the n : p ratio isn't relevant here, n is relevant)
- you can check the stability of your modeling approach: if the 10 outer folds arrive essentially at equal models, then your training is stable. If these models vary wildly (not all differences are harmful, though. See also other question), you may need to go back and adapt your hyperparameter (incl. model selection) heuristic to stabilize your whole training procedure.