So long as the underlying specification is correct, multicollinearity does not actually bias results; it just produces large standard errors in the related independent variables.
Methods for fitting linear models with multicollinearity have been developed; some require additional assumptions such as effect sparsity that a large fraction of the effects are exactly zero.
Multicollinearity does not reduce the predictive power or reliability of the model as a whole, at least within the sample data themselves; it only affects calculations regarding individual predictors.
