In the case of Linear Regression models, some data has residual terms influenced by a predictor term (heteroscedasticity). Taking a case where the residual increases with increase in predictor value, transforming the data using a concave function like log Y might help.
But how does this impact other aspects of the model? Is there a disadvantage? Is a weighted least squares fit better in this case?