The "Model #" controls don't refer the primary/secondary dataset. They're actually used to mix several semivariogram models together into a single new semivariogram. For example, you can use the Stable model (which is the default), or you can change it to, for example, Spherical. But you can also create a new semivariogram that is an average between Stable and Spherical (weighted averages of valid semivariograms are themselves valid semivariograms) by providing one semivariogram into Model 1 and the other into Model 2 (and even a third into Model 3). This feature isn't often used, but my understanding is that it is useful in situations where the data are affected by two different processes, one short-range and the other long-range. In that case, you can mix one semivariogram with a short range with another semivariogram with a long range, and the resulting average will usually be better than either of the components individually.
As for how to tell the difference between the primary and secondary datasets, look for "Var 1" and "Var 2" (primary and secondary). For example the semivariogram display for "Var 1 - Var 1" shows the semivariogram for the primary variable. "Var 2 - Var 2" shows the semivariogram for the secondary dataset. "Var 1 - Var 2" shows the cross covariance. There will be similar "Var #" labels for the parameters on the right to distinguish between the three models.
