This question may well be too open-ended to get a definitive answer, but hopefully not.
Machine learning algorithms, such as SVM, GBM, Random Forest etc, generally have some free parameters that, beyond some rule of thumb guidance, need to be tuned to each data set. This is generally done with some kind of re-sampling technique (bootstrap, CV etc) in order to fit the set of parameters that give the best generalisation error.
My question is, can you go too far here? People talk about doing grid searches as so forth, but why not simply treat this as an optimisation problem and drill down to the best possible set of parameters? I asked about some mechanics of this in this question, but it hasn't received much attention. Maybe the question was badly asked, but perhaps the question itself represents a bad approach that people generally do not do?
What bothers me is the lack of regularisation. I might find by re-sampling that the best number of trees to grow in a GBM for this data set is 647 with an interaction depth of 4, but how sure can I be that this will be true of new data (assuming the new population is identical to the training set)? With no reasonable value to 'shrink' to (or if you will, no informative prior information) re-sampling seems like the best we can do. I just don't hear any talk about this, so it makes me wonder if there is something I'm missing.
Obviously there is a large computational cost associated with doing many many iterations to squeeze every last bit of predictive power out of a model, so clearly this is something you would do if you've got the time/grunt to do the optimisation and every bit of performance improvement is valuable.