Cross Validation and bias relation I found a question (Question 7) here:

Question: For k cross-validation, larger k value implies more bias
 Options:  True or False

My answer is: True. 
Reason:
Larger K means more folds means smaller test set which means larger training set. As you increase training data you bring down variance which means increase bias.
So as K increases --&gt; Training data size increases --&gt; Variance reduces --&gt; Bias increases
Hence answer is True
But the website says answer is False. 
Can someone explain if my logic is wrong and why their answer is right?
Why should bias increase as the training set size increases?
My intuition is that as you increase K, your test sets get smaller, increasing the variance of your evaluation metric. At the same time, you're fitting your model to more and more similar training data (since the training set in each fold is approaching the full set), so you're more likely to overfit to the training data because you're fitting your candidate models on almost the entire training data set in each fold.