$k$-fold cross-validation simply repeats the same process with different parts of the data. Therefore any difference between different folds can only be due to chance, i.e. it's only because different instances are selected (by chance) that the results are different.
In theory, if the dataset is large and representative enough, the performance should be almost identical across different folds. Thus large differences tend to indicate that the data is not representative enough and/or that the trained model overfits (i.e. it's too complex for the data, learning detail which happen by chance instead of general patterns).
Imho this graph is a bit subjective to interpret: I would say that there are quite important variations across folds, so it could be a case of overfitting. But the different curves stay roughly around the same area, so I think it's not too bad. It's a borderline case from my point of view.