Nested Cross Validation - Which Models Should We Evaluate in the Outer Loop? Lets assume for example that I am attempting to predict a binary outcome using p predictors in which n>p with methods including a LASSO Regression, a Logistic Regression and SVM with an RBF kernel. Lets also assume that I plan to use nested cross-validation for model selection and generalization error estimation. 
My question is:
Should we estimate performance in the outer loop only for the best model? What if I estimate performance in the outer loop for the logistic regression model, the best LASSO model and best SVM model from the inner loop? Will then selecting one of these 3 'best' models for production based on the generalization error of the outer loop lead to bias? It is sometimes the case that if a more interpretable model such as a logistic model has only a slight decrease in performance compared to a more complex model, one might prefer the more interpretable model at the cost of some performance - hence, the motivation to estimate generalization error for more than just the best performing model from the inner loop.
 A: 
Should we estimate performance in the outer loop only for the best model? 

yes. more precisely: in each of teh outer loop's folds, for the model chosen by its inner CV. 

What if I estimate performance in the outer loop for the logistic regression model, the best LASSO model and best SVM model from the inner loop? 

Then you know that these three modelling Ansätze perform similarly well for your data compared to the uncertainty / instability of the optimization. 
This may mean that your optimization failed. OTOH, unlike optimizing model complexity where we expect one global optimum, there really isn't any reason why different modeling heuristics should not do similarly well on the same data. So it may just mean that it doesn't matter whether you do LR or SVM in terms of your figure of merit.
see also my answers to related questions: https://stats.stackexchange.com/a/245169/4598 and https://stats.stackexchange.com/a/65156/4598

Will then selecting one of these 3 'best' models for production based on the generalization error of the outer loop lead to bias? 

yes

It is sometimes the case that if a more interpretable model such as a logistic model has only a slight decrease in performance compared to a more complex model, one might prefer the more interpretable model at the cost of some performance

You can build that into your selection heuristic in the inner loop.
