I don't see how "do nothing" is a good option. It is explained in the rest of the comment. I would suggest you read that. For a more in-depth explanation, th

I don't see how "do nothing" is a good option. It is explained in the rest of the comment. I would suggest you read that. For a more in-depth explanation, this also explained in most econometrics textbooks. Stability of the parameters over time (or over different training sets) is one of the most important properties of a good model. In some cases it is, in many cases unbiasedness or consistency are more important. When stability is a priority, collinearity clearly needs to be addressed. If unbiasedness and/or consistency is a priority, doing nothing is the best option (since removing variables leads to omitted variable bias, PCA does not yield the parameter of interest, and regularization techniques such as ridge regression are both biased and inconsistent).