GLS, or Generalized Least Squares, is a statistical technique used to estimate the parameters of a linear regression model. It is particularly useful when the assumptions of ordinary least squares (OLS) are violated, such as when the errors have non-constant variance or are correlated. By accounting for these issues, GLS provides more efficient and unbiased estimates. In GLS, the model is adjusted to account for the structure of the error terms, often using a covariance matrix. This allows for better handling of data that may not meet the standard assumptions of linear regression, making it a valuable tool in econometrics and various fields of research, including finance and social sciences.