Always start by visually inspecting the data after applying each model.
This will give you a good idea of how well each model is detrending the data.
Look for any remaining trend, oscillations, or patterns that might suggest that the model is not fully capturing the underlying trend in the data.
It's always a good idea to consider any domain-specific knowledge that might help you select the best detrending model.
For example, if you are modeling physical newspaper sales, you might expect a downward trend over time.
If you are detrending as a preprocessing step for a machine learning model, you can compare different models considering the performance of the full model pipeline on the validation set.