Compelling cases for OR against Object-Oriented design in an ML code engine?

I’m curious to hear opinions on why strict adherence to OOP design practices is either a help or a hindrance in data science development, as compared to more procedural or functional development design. Please provide concrete examples or external resources where necessary. Also, please keep examples specific to data science development whenever possible.

For context, when I say ML engine, I mean a code base that does everything from ETL, to feature engineering, feature selection, model training, model evaluation, production …

cases code datascience design ml object-oriented