Transformations of X can linearize the relationship with Y, but often do not change the distribution of error terms (i.e., does not correct heteroskedascity).

Transformations of Y can linearize the relationship with X, and will change the distribution of error terms (i.e., can correct heteroskedascity).

I base my decision on which to transform on whether I must correct for heteroskedascity or not. Log transforming both X and Y results in a stronger impact in linearizing the relationship. Note: This is strictly to linearize a relationship. In other cases the choice of log-linear versus log-log has a specific interpretation.