# Logistic Regression - Multicollinearity Concerns/Pitfalls

In Logistic Regression, is there a need to be as concerned about multicollinearity as you would be in straight up OLS regression?

For example, with a logistic regression, where multicollinearity exists, would you need to be cautious (as you would in OLS regression) with taking inference from the Beta coefficients?

For OLS regression one "fix" to high multicollinearity is ridge regression, is there something like that for logistic regression? Also, dropping variables, or combining variables.

What approaches are reasonable for reducing the effects of multicollinearity in a logistic regression? Are they essentially the same as OLS?

(Note: this is not for the purpose of a designed experiment)