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:
During the triaging meeting, we decide that this feature is interesting, but not high priority. We do welcome upvotes on this issue to gauge interest. On this note, the PR https://github.com/scikit-learn/scikit-learn/pull/23740 has [an example](https://output.circle-artifacts.com/output/job/8735e9ab-a82e-4503-8715-ba42ed222f88/artifacts/0/doc/auto_examples/model_selection/plot_grid_search_text_feature_extraction.html) that demonstrates how to use plotly's `parallel_coordinates` with grid search results.

For me, I think parallel coordinate plots work well only if it is interactive, which means depending on a library such as plotly. A matplotlib version would work within scikit-learn's current constraints, but it creates a static image. There are ways to make [matplotlib interactive](https://matplotlib.org/stable/users/explain/interactive.html?highlight=interactive), but it only works for specific backends.

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