Why to Remove Multicollinearity?

Multicollinearity reduces the precision of the estimated coefficients, which weakens the statistical power of your regression model. You might not be able to trust the p-values to identify independent variables that are statistically significant.

Why multicollinearity is a problem?

Multicollinearity is a problem because it undermines the statistical significance of an independent variable. Other things being equal, the larger the standard error of a regression coefficient, the less likely it is that this coefficient will be statistically significant.

Why do we remove correlated variables?

The only reason to remove highly correlated features is storage and speed concerns. Other than that, what matters about features is whether they contribute to prediction, and whether their data quality is sufficient.