Correlated predictors do not automatically break a linear regression model. Standard sources agree that ordinary least squares can still be estimated and predictions may remain useful, especially when collinearity is moderate. The main caveat is that stronger collinearity often inflates standard errors and makes individual coefficients unstable or hard to interpret, and perfect collinearity prevents unique estimation.