I have a question about model selection using cross validation.
As far as I understood from many other replies related to model selection here, one should use nested cross validation in order to properly (1) select and then (2) assess a model. But almost all these questions were about relatively small data sets, so I assumed that this method is good for relatively small data sets.
My question is what should one do in the case of a large data set ($\sim18$ millions observations, trying to select input variables out of $\sim10^2$ features), since training one model on such data set could be quite time consuming? Would it be fair to leave out test set for model assessment (after CV is done) and perform cross validation on rest of the data to select a model?