AUC for someone with no stats knowledge Can someone explain what area under the curve means for someone with absolutely no stats knowledge? For example, if a model claims an AUC of 0.9, does that mean that it makes an accurate prediction 90% of the time?

A classifier is a criterion to assign an individual to a category ("positive" or "negative") depending to some of its characteristics.
Some classifiers will provide each individual with a number between $0$ and $1$, with $0$ being "totally sure it's negative" and 1 being "totally sure it's positive". We usually take $0.5$ as the threshold between what we take as "positive" and what we take as "negative", but this is not always the case.
Taking a low threshold will result in more true positives but also more false ones. Taking a higher threshold will reduce the number of false positives, but we'll also leave as negative some of the cases that where actually positive (thus less true postiives as well). So in the end, since no classifier is perfect, it will be a compromise between the two.
Each point in the ROC curve represents the rates of true and false positives for each of the possible thresholds we could choose. The AUC is the area below that curve. A high AUC indicates that the model can get a good FPR (false positive rate) without losing too much TPR (ture positive rate) and vice-versa.(Note that the area below the ROC curve will be big if you get a high TPR already for an FPR close to 0).
SIMPLIFIED EXAMPLE: let's say you want to use a person's height to determine whether they're a man or a woman. Your classifier will choose some height $X$ and predict that everyone above height $X$ is male and everyone below it is female.
If you choose a very high $X$, like $1.90$m, you will hardly ever mislabel a woman as male, but you will also "miss" many men. On the other hand, if you pick a low $X$ like $1.50$m, you will correctly identify almost all men, but you will also classify a lot of women as male. For each $X$ you can choose, you'll get different true and false positive ratios, but it's ultimately a kind of arbitrary choice depending of what type of error worries you the most.
In this context, we could plot the ROC curve with the different TPRs and FPRs, then the AUC would give us an idea of how good of a classifier we can hope to get using height (as opposed to some other classifier we could have thought of using something like weight, age, blood pressure...). (See user215517's answer)
