Right. In particular, using least squares is justified by the assumption that errors are normally distributed, in which case least squares yields the maximum-likelihood estimate. Because boxcox is intended to transform a random variable into one that is normally distributed, the model assumptions are actually more likely to be satisfied if you do regression on the transformed values. (Which was probably the reason boxcox was invented in the first place.)