Under Ensemble you can use Majority Votes, Average, Weights etc to get the final outcome from Ensemble model. To understand it better you can go through this Link, explained well by Alexander.
Now, let us consider that you have 3 models which has an accuracy of 65-70%. Now by stacking these 3 models there is very high chance that you models accuracy would increase. In another scenario you have 3 models model-1: 95%, model-2: 55%, model-3: 45% accuracy, then if you stack them then there is a very good chance it can worsen the result.
Conclusion, it all depends on the individual models performance, Ensemble performs well when you combine moderately performing models.
Technically there is no proof saying that this method is suitable for this scenario but trail and error might help you to get good results. It is subjective to the business scenario. Similarly, for bagging and boosting.
In my experience with Bagging when the model accuracy is bad, I tried using bagging to fit the data better but EOD training accuracy(20% to 10% approx) was decreased but test accuracy was worsened(11% to 20% approx). So, you have to decide which suits your business problem better and take it forward.