Cross validation vs leave one out
Assuming your dataset includes $k$ samples:

In cross-validation, there are $N$ partitions, and the test split for each partition will have size $\frac{k}{N}$.

Leave-one-out validation is a special type of cross-validation where $N = k$.  You can think of this as taking cross-validation to its extreme, where we set the number of partitions to its maximum possible value.  In leave-one-out validation, the test split will have size $\frac{k}{k} = 1$

It's easy to visualize the difference. Here's two figures which contrast cross-validation and leave-one-out.  In these figures, each sample in the dataset is represented by a colored circle. The training set is represented by the green circles, and the testing set is represented by the yellow circles.

5-fold cross validation:



Leave-one-out cross validation: