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From Wikipedia:
In k-fold cross-validation, the original sample is randomly
partitioned into k equal size subsamples.
I am working on a 10 fold cross validation project. I have a dataset that has 76 elements. It means that I can not have equal size partitions.
What are the approaches for remaining data (in my example 6 data)? Ignoring them, making a data 16 elements, 6 partitions have 11 elements or etc?
You do not ignore them !!
Simply, you may distribute the 6 data points randomly over the 10 folds.
Another way is to just add them to any of the folds. At the end, these points will be passed for testing once as supposed to be done.