The reason to use a full machine learning model instead of a simple statistical model is three-fold:
1) The framework supports any arbitrary JSON encodable data structure as a variant, this includes nested dictionaries, lists, etc. A full ML model allows capturing the entire complexity of the data stored in the variant - numbers, strings, booleans, etc and those can even be changed and the model will still be able to predict performance.
2) A full ML model allows generalization across complex variants. So if later on you introduce new variants, it may already be imply some things about how that variant will perform. For the most part simple statistical models must learn each variant from scratch.
3) The models don't just seek to make decisions that are a global optimum, like you would do with A/B testing. They are seeking to find a contextual optimum given the current conditions. This type of modeling also requires the generalization that ML gives you.