Does multicollinearity affect performance of a classifier? I know that wikipedia references are sometimes frowned upon here, but this one has me puzzled:  Wikipedia - Multicollinearity
I know what multicollinearity is, and today I tried figuring out how/if it would affect performance of machine learning models.  
At the beginning of the article, it says 

Multicollinearity does not reduce the predictive power or
  reliability of the model as a whole

...but, as I read on it says that 

A principal danger of such data redundancy is that of overfitting in
  regression analysis models

and I know that overfitting increases variance greatly, and can degrade performance severely.
Are either of these or both of these statements wrong?
 A: There is an important qualifier in the continuation of the first cited quote from Wikipedia: "Multicollinearity does not reduce the predictive power or reliability of the model as a whole, at least within the sample data set" (emphasis added).
Unless there is a singular design matrix, multicollinearity does not prevent fitting a model to an individual data sample. I take that to be the point of the first Wikipedia quote. A regression model can capture the data sample well, including all of the peculiarities and noise of the particular data sample, in the presence of multicollinearity.
The problems arise when you try to apply the model outside the original data set to the underlying population. Multicollinearity will severely affect performance outside of the original data sample, as you recognize.
A: Contrary to @EdM's answer, based on the answer here, the performance will not degrade if the test set has the same covariance matrix. In different words, if the correlation between the variables in the test set is the same, the combination of the coefficients and the feature vectors will lead to a valid result. Often this is the case.
