While least squares regression is widely used, it has some limitations. It assumes linearity, constant variance and independence of observations, which may not always hold true. It is also sensitive to outliers. These issues can be addressed by examining residuals, conducting various statistical tests and considering robust or non-linear regression methods when appropriate. In the presence of multiple predictors, one might also need to consider potential multicollinearity.