The goal of $\nu$ is similar to that of $C$. The parameter $\nu$ is there to finetune the trade-off between overfitting and generalization. The problem with $C$ is that it is positive and unbounded which makes it difficult to choose optimally during cross-validation. Thus, the range $[0,1]$ makes the regularization more interpretable in terms of $\nu$ but empirically, people have found it more difficult to optimize in terms of $\nu$.
The relationship between $\nu$ and $C$ is given by the formula $\nu= \frac{A+B}{C}$ where $A$ and $B$ are some constants that are not easy to calculate.
The explanation/intuition about the interpretation of $\nu$ given in the link you've mentioned is pretty accurate. $\nu$ is upper bounded by the fraction of outliers and lower bounded by the fraction of support vectors. Just consider that for default value $0.1$, atmost $10\%$ of the training samples are allowed to be wrongly classified or can be considered as outliers by the decision boundary. And atleast $10\%$ of your training samples will act as support vectors (points on the decision boundary).
