Is it a good idea to use the mean and standard deviation of coefficients from other models as my prior in Bayesian Regression?

I have a dataset that I’ve been playing around with for school I have gotten very good results with a bunch of methods (Ridge, Lasso, ElasticNet, SVM, Bagging, Stacking and NN even)

Now I’m having a range of different coefficients of my predictors, is it a good idea to use them as my priors (I did so, I think the result has been ok) or should I use noninformative priors instead.

If it is a bad idea, could you explain why?

Or if different use cases, when does it make sense to use one or the other? I'm using Pymc3's sampler and GLM.

Thanks in advance for any pointers and explanations.