# Understanding the deviance and pseudo-R2 from a GLM

I'm investigating some measures of model performance I can use for my (poisson) GLM models and came across a McFadden pseudo R2:

$$R^2 = 1 - \frac{\text{Residual deviance}}{\text{Null deviance}},$$

I then went on to read here (p23) that:

this shouldn't be used to compare models which have a different number of parameters on an 'in-sample' dataset because there is no adjustment for the number of degrees of freedom.

Conceptually I think I understand this because deviance always reduces when you add more parameters so models with different number of parameters are not directly comparable. Is that correct?

The author makes the distinction here for the 'in-sample' dataset - does this mean that this measure can be a useful when assessing the hold-out performance of two models with different numbers of parameters? If so, how do we explain this?