The following is the definition of multicollinearity according to Wikipedia (https://en.wikipedia.org/wiki/Multicollinearity):
Multicollinearity is a phenomenon in which two or more predictor variables in a multiple regression model are highly correlated, meaning that one can be linearly predicted from the others with a substantial degree of accuracy.
Basically, let's say you have a model with three predictors:
And one of the predictors is a linear combination (perfect multicollinearity) or is approximated by a linear combination (near multicollinearity) of two other predictors.
Here, is some noise ...