Multicollinearity occurs in regression analysis when two or more predictor variables are highly correlated, making it difficult to isolate the individual effect of each predictor on the response variable. This can lead to inflated standard errors and unreliable statistical inferences, complicating model interpretation and reducing the precision of estimated coefficients.