It depends on the model but, basically, the new levels have no effect on the model fit. They mostly help avoid errors when predicting.
If there is a linear regression via lm(), the new level will be all zeros and the coefficient will be NA. For trees, the level will get bundled into splits with other levels. For example, a factor with 5 known levels and one new level might be split as {a,c,d} vs {b,e,new}. In this situation, there's no way to tell where it will end up.
There are some feature preprocessors that can estimate new levels (in the embed package) and those might be able to assign a value to the new level.
tl;dr
There is no effect on the model and perhaps not even the predictions. step_novel() helps avoid errors.