I recently had a (more experienced) coworker tell me that when you have a large enough sample, training error should be "good enough" to assess model performance. His point was that with a sufficiently large sample, resampling techniques become very computationally intensive while at the same time the training error rate becomes more accurate - and so it's not worth it to cross-validate.
For reference, we're working with traditional GLMs and GLMMs, with a dataset of about 20 million observations. A 10-folds cross-validation run for several different models takes a couple of hours to complete.
Is he right in that I'm wasting time with cross-validation?