Multicollinearity is a statistical phenomenon in which there is variable correlation between multiple variables in a model. It occurs when two or more variables are highly correlated with each other, which can lead to bias in estimates. Multicollinearity can affect the precision of estimates and make it more difficult to determine the individual effects of a variable on the model outcome.