When I was in my postdoc (applied human genetics), my advisor's rule was that you needed to understand the tools you were using at a layer of abstraction below your interface with them.
For example, if we wanted to conduct an analysis with a new piece of software, it wasn't enough to run the software: we needed to be able to explain the theory behind it (basically, to be able to rewrite the tool).
From that standpoint, I think that even if you keep with #2, you might benefit from taking steps to gain the understanding from #1. It will help you understand the models' real advantages and disadvantages to help you decide how to incorporate them in #2.
For example, if we wanted to conduct an analysis with a new piece of software, it wasn't enough to run the software: we needed to be able to explain the theory behind it (basically, to be able to rewrite the tool).
From that standpoint, I think that even if you keep with #2, you might benefit from taking steps to gain the understanding from #1. It will help you understand the models' real advantages and disadvantages to help you decide how to incorporate them in #2.