Multicollinearity means predictor variables are correlated with each other, making it harder to determine the role each of the correlated variables is playing. Mathematically, it means the standard errors are increased. Multicollinearity can have counter-intuitive effects.
As a follow up to the excellent answers provided for: Does the order of explanatory variables matter when calculating their regression coefficients? (Which I've found incredibly useful from a ...