Feature Request - Parallel Coordinates Plot for GridSearch result analysis

### Describe the workflow you want to enable

GridSearch result are hard to analyze expecially when `param_grid` is very large.

The current documentation show usages of:
- [matrix_plot/pivot](https://scikit-learn.org/stable/_images/sphx_glr_plot_successive_halving_heatmap_001.png). That can visualize the relationship beetween 2 params for 1 metrics (2D).
- [line plot](https://scikit-learn.org/stable/_images/sphx_glr_plot_multi_metric_evaluation_001.png). Each line represent a 1D (1 params vs 1 metrics) relationship and by taking multiple line when can visualize at least 2D relationship, but this became mess for more.
- [box plot](https://scikit-learn.org/stable/_images/sphx_glr_plot_compare_reduction_001.png). Same conclusion as for line plot but using box instead.

None of these plot can represent nicely more then a 2D relationship. 3D, 4D and more can be visualize by making a bunch of basic 2D plot, but the number plot will easily became huge and we lost relationship beetween some `params`. This get even worse with `RandomizedSearchCV` as the params are not evenly compute.

One common way to represent N-D relationship without loss is to use **Parallel Coordinates Plot**:

![Example of Parallel Coordinates Plot of the proposed implementation](https://user-images.githubusercontent.com/11278197/187024865-d6183eac-8cac-45fd-a629-2a2e84daffe2.png)

The current implementation of PC Plot are:
- [Plotly express](https://plotly.com/python/parallel-coordinates-plot/). But it don't integrate well with the `param_grid` format. Plus is not matplotlib compatible.
- Tensorboard and deep learning experiement tracker (wandb ...). Same don't integrate weel, not matplotlib stack and need even more conversion work and a web server (or cloud account :()
- Handmade matplotlib implementation. Matplotlib compatible, but at the time we don't have any easy to use implementation from common package. + with `param_grid` format compatibility.

### Describe your proposed solution

I presently use my own implementation of matplotlib PC Plot (see the image above) for my work.
The current interface is:
```python
def plot_parallel_coordinates(
        grid_model: GridSearchCV, 
        params: list[str] = ["params"],
        scoring: str = ["mean_test_score"]
        cmap : Optional[str] = None,
        ax: Optional[plt.Axes] = None) -> plt.Axes
```

If think the community could benefith from using a common and scikit-learn integrated implementation (for example in `metrics.ParallelCoordinatesPlot` or `inspection.ParallelCoordinatesPlot` and with `plot_parallel_coordinates` function)

### Describe alternatives you've considered, if relevant

_No response_

### Additional context

Please give 👍 if you think this could be benefith, so we can start discuss implementation.

COMMENT:
> Hey @julien-blanchon! I work at wandb, curious what you meant by:
> 
> > deep learning experiement tracker (wandb ...). Same don't integrate weel
> 
> I think it should be possible to create your own parallel coordinates plot with wandb no? Or is it the sklearn & wandb integration that isn't working for you? Happy to see if I can help here :)

Hi @morganmcg1, sklearn wandb integration work well ! I was talking about the Hyperparameter Sweeps integrated parallel coordinates chart (https://wandb.ai/site/articles/introduction-hyperparameter-sweeps). It doest the jobs but I need "extensive" work to make it work and you need wandb (which a bunch of other things) anyway to visualize your parallel plot. My proposition was more like adding a small function as a sklearn plot to easily generate matplotlib/jpeg plot

Labels: New Feature, module:inspection, Needs Decision - Include Feature