Understaning Bayesian Optimisation graph

I came across the concept of Bayesian Occam Razor in the book Machine Learning: a Probabilistic Perspective. According to the book:

Another way to understand
the Bayesian Occam’s razor effect is to note that probabilities must
sum to one. Hence $sum_D’ p(D’ |m) = 1$, where the sum is over all possible data sets. Complex
models, which can predict many things, must spread their probability mass thinly, and hence
will not obtain as large a probability for any given data set as simpler models. This is sometimes called the conservation of probability mass principle.

The figure below is used to explain the concept:

Image Explanation: On the vertical axis we plot the predictions of 3 possible models: a simple one, $M_1$ ; a medium one, $M_2$ ; and a complex one, $M_3$ . We also indicate the actually observed
data $D_0$ by a vertical line. Model 1 is too simple and assigns low probability to $D_0$ . Model 3
also assigns $D_0$ relatively low probability, because it can predict many data sets, and hence it
spreads its probability quite widely and thinly. Model 2 is “just right”: it predicts the observed data with a reasonable degree of confidence, but does not predict too many other things. Hence model 2 is the most probable model.

What I do not understand is when a complex model is used, it will likely overfit data and hence the plot for a complex model will look like a bell shaped with its peak at $D_0$ while simpler models will more likely have a broader bell shape. But the graph here shows something else entirely. What am I missing here?