What is the industry standard for the level of technical detail when writing about machine learning for a paper in a science journal?
The following is the standard for deep learning literature as far as I am aware:


  Should I include every model we tried including those that we didn't?


No, only include your best model (or at least, the ones you want to show). Journal space is limited, so people generally don't include unsuccessful attempts.


  Should I include hyperparameters for gridsearch or only the best hyperparameters?


Only the best hyperparameters. If one of your model's selling points is it's resilience to different hyperparameters then you might include a diagram showing how the different hyperparameters affect the results. If not then the worse-performing hyperparameters are irrelevant.


  Should I include every method of feature engineering we implemented?


Yes, this is important for reproduction.


  Should I include examples of the data's structure?


That would be nice, but perhaps not necessary. Perhaps include it if your data is very non-standard.