Pseudo \(R^2\) fit statistics for generalized linear models take on similar values to their ordinary least squares counterparts, but are based on maximum likelihood estimates instead of sums of squares. In general, higher values indicate that the model is better at discriminating.
T.J. Smith and C.M. McKenna. A comparison of logistic regression pseudo R^2 indices. Multiple Linear Regression Viewpoints, 39(2):17–26, 2013.