Generalized Least Squares (GLS) is an extension of Ordinary Least Squares (OLS) that accounts for heteroscedasticity or correlation in the error terms, providing more efficient and unbiased parameter estimates when these assumptions are violated. By transforming the model or using a weighted approach, GLS minimizes the sum of squared residuals in a way that accounts for the variance structure of the errors, leading to more reliable statistical inferences.