I think its more intuitive for statistical applications where Python is grossly under-represented. This includes things like the design and analysis of experiments but also lots of domain specific statistics and algorithms such as in bioinformatics, chemistry, and so on.
Typically those applications are not the sort of line-of-business enhancements ML in Python is more tuned to. I.e. recommender systems, NN models, and so on.
The consistency of model specification across multiple libraries is really helpful (base lm, lme4, brms etc). Even though the syntax is sometimes extended, it seems consistent enough to mostly be comprehensible/guessable.
Typically those applications are not the sort of line-of-business enhancements ML in Python is more tuned to. I.e. recommender systems, NN models, and so on.