Questions on ensemble technique in machine learning
Instead, model 2 may have a better overall performance on all the data
  points, but it has worse performance on the very set of points where
  model 1 is better. The idea is to combine these two models where they
  perform the best. This is why creating out-of-sample predictions have
  a higher chance of capturing distinct regions where each model
  performs the best.


It's not about training on all the data or not. Both models trained on all the data. But each of them is better than the other at different points. If I and my older brother are tying to guess the exact year of a song, I will do better in 90s songs and he in 80s songs - it's not a perfect analogy but you get the point - imagine my brain just can't process 80s songs, and his can't process 90s songs. The best is to deploy us both knowing we each have learnt different regions of the input space better. 


  Simply, for a given input data point, all we need to do is to pass it
  through the M base-learners and get M number of predictions, and send
  those M predictions through the meta-learner as inputs


k-fold is still just one learner. But you're training multiple times to chose parameters that minimize error in the left-out fold. This is like training only me on all the songs showing me k-1 folds of data, and I calibrate my internal model the best I can... but I'll still never be very good at those 80s songs. I'm just one base learner whose functional form (my brain) isn't fit for those songs. If we could bring the second learner along, that would improve things.